Overview

Dataset statistics

Number of variables49
Number of observations5031
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory323.0 B

Variable types

Numeric30
Categorical19

Alerts

year is highly overall correlated with inflation_rate and 10 other fieldsHigh correlation
month is highly overall correlated with quarterHigh correlation
inflation_rate is highly overall correlated with year and 10 other fieldsHigh correlation
Average_income is highly overall correlated with me_mln_flagHigh correlation
apartments_lt_5lvl is highly overall correlated with apartments_me_5lvl_lt_10lvlHigh correlation
apartments_me_5lvl_lt_10lvl is highly overall correlated with apartments_lt_5lvl and 10 other fieldsHigh correlation
apartments_me_10lvl_lt_15lvl is highly overall correlated with apartments_me_5lvl_lt_10lvl and 5 other fieldsHigh correlation
apartments_me_15lvl is highly overall correlated with apartments_me_10lvl_lt_15lvl and 2 other fieldsHigh correlation
dormitory is highly overall correlated with apartments_me_5lvl_lt_10lvl and 4 other fieldsHigh correlation
hotel is highly overall correlated with commercial and 2 other fieldsHigh correlation
commercial is highly overall correlated with apartments_me_5lvl_lt_10lvl and 5 other fieldsHigh correlation
office is highly overall correlated with apartments_me_5lvl_lt_10lvl and 7 other fieldsHigh correlation
retail is highly overall correlated with apartments_me_5lvl_lt_10lvl and 4 other fieldsHigh correlation
college is highly overall correlated with apartments_me_5lvl_lt_10lvl and 1 other fieldsHigh correlation
school is highly overall correlated with apartments_me_5lvl_lt_10lvl and 10 other fieldsHigh correlation
university is highly overall correlated with apartments_me_5lvl_lt_10lvl and 7 other fieldsHigh correlation
fast_food is highly overall correlated with apartments_me_5lvl_lt_10lvlHigh correlation
leisure is highly overall correlated with apartments_me_5lvl_lt_10lvl and 3 other fieldsHigh correlation
me_mln_flag is highly overall correlated with Average_income and 3 other fieldsHigh correlation
lt_100k_flag is highly overall correlated with schoolHigh correlation
quarter is highly overall correlated with monthHigh correlation
2014 is highly overall correlated with year and 1 other fieldsHigh correlation
2015 is highly overall correlated with year and 1 other fieldsHigh correlation
2016 is highly overall correlated with year and 1 other fieldsHigh correlation
2017 is highly overall correlated with year and 1 other fieldsHigh correlation
2018 is highly overall correlated with year and 1 other fieldsHigh correlation
2019 is highly overall correlated with year and 1 other fieldsHigh correlation
2020 is highly overall correlated with year and 1 other fieldsHigh correlation
2021 is highly overall correlated with year and 1 other fieldsHigh correlation
2022 is highly overall correlated with year and 1 other fieldsHigh correlation
2023 is highly overall correlated with year and 1 other fieldsHigh correlation
food_court is highly imbalanced (66.6%)Imbalance
exhibition_centre is highly imbalanced (89.4%)Imbalance
2013 is highly imbalanced (97.8%)Imbalance
2014 is highly imbalanced (89.7%)Imbalance
2015 is highly imbalanced (75.2%)Imbalance
2016 is highly imbalanced (59.3%)Imbalance
2021 is highly imbalanced (53.2%)Imbalance
2022 is highly imbalanced (53.8%)Imbalance
2023 is highly imbalanced (91.3%)Imbalance
month has 433 (8.6%) zerosZeros
start_indx has 427 (8.5%) zerosZeros
apartments_lt_5lvl has 135 (2.7%) zerosZeros
apartments_me_5lvl_lt_10lvl has 56 (1.1%) zerosZeros
apartments_me_10lvl_lt_15lvl has 492 (9.8%) zerosZeros
apartments_me_15lvl has 1127 (22.4%) zerosZeros
dormitory has 926 (18.4%) zerosZeros
hotel has 1733 (34.4%) zerosZeros
residential has 679 (13.5%) zerosZeros
commercial has 54 (1.1%) zerosZeros
office has 599 (11.9%) zerosZeros
civic has 2785 (55.4%) zerosZeros
college has 1745 (34.7%) zerosZeros
school has 59 (1.2%) zerosZeros
transportation has 3155 (62.7%) zerosZeros
university has 1790 (35.6%) zerosZeros
stadium has 4431 (88.1%) zerosZeros
sports_hall has 4435 (88.2%) zerosZeros
pavilion has 4029 (80.1%) zerosZeros
bar has 3666 (72.9%) zerosZeros
fast_food has 1229 (24.4%) zerosZeros
cafe has 517 (10.3%) zerosZeros
restaurant has 1365 (27.1%) zerosZeros
cinema has 3062 (60.9%) zerosZeros
tourism has 1063 (21.1%) zerosZeros

Reproduction

Analysis started2023-06-13 13:52:35.988237
Analysis finished2023-06-13 13:53:30.491897
Duration54.5 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.7392
Minimum2013
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:30.530970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2015
Q12017
median2019
Q32020
95-th percentile2022
Maximum2023
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0182001
Coefficient of variation (CV)0.00099973295
Kurtosis-0.54092477
Mean2018.7392
Median Absolute Deviation (MAD)1
Skewness-0.095860341
Sum10156277
Variance4.0731317
MonotonicityNot monotonic
2023-06-13T16:53:30.582619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2019 943
18.7%
2018 907
18.0%
2020 745
14.8%
2017 692
13.8%
2021 501
10.0%
2022 492
9.8%
2016 410
8.1%
2015 207
 
4.1%
2014 68
 
1.4%
2023 55
 
1.1%
ValueCountFrequency (%)
2013 11
 
0.2%
2014 68
 
1.4%
2015 207
 
4.1%
2016 410
8.1%
2017 692
13.8%
2018 907
18.0%
2019 943
18.7%
2020 745
14.8%
2021 501
10.0%
2022 492
9.8%
ValueCountFrequency (%)
2023 55
 
1.1%
2022 492
9.8%
2021 501
10.0%
2020 745
14.8%
2019 943
18.7%
2018 907
18.0%
2017 692
13.8%
2016 410
8.1%
2015 207
 
4.1%
2014 68
 
1.4%

month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49644929
Minimum0
Maximum1
Zeros433
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:30.626778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.18181818
median0.45454545
Q30.72727273
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.54545455

Descriptive statistics

Standard deviation0.31647466
Coefficient of variation (CV)0.63747631
Kurtosis-1.2338508
Mean0.49644929
Median Absolute Deviation (MAD)0.27272727
Skewness0.02150555
Sum2497.6364
Variance0.10015621
MonotonicityNot monotonic
2023-06-13T16:53:30.671124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 437
8.7%
0 433
8.6%
0.09090909091 432
8.6%
0.1818181818 431
8.6%
0.2727272727 429
8.5%
0.7272727273 421
8.4%
0.4545454545 419
8.3%
0.6363636364 419
8.3%
0.9090909091 412
8.2%
0.3636363636 409
8.1%
Other values (2) 789
15.7%
ValueCountFrequency (%)
0 433
8.6%
0.09090909091 432
8.6%
0.1818181818 431
8.6%
0.2727272727 429
8.5%
0.3636363636 409
8.1%
0.4545454545 419
8.3%
0.5454545455 389
7.7%
0.6363636364 419
8.3%
0.7272727273 421
8.4%
0.8181818182 400
8.0%
ValueCountFrequency (%)
1 437
8.7%
0.9090909091 412
8.2%
0.8181818182 400
8.0%
0.7272727273 421
8.4%
0.6363636364 419
8.3%
0.5454545455 389
7.7%
0.4545454545 419
8.3%
0.3636363636 409
8.1%
0.2727272727 429
8.5%
0.1818181818 431
8.6%

start_indx
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49704559
Minimum0
Maximum1
Zeros427
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:30.715340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.18181818
median0.45454545
Q30.72727273
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.54545455

Descriptive statistics

Standard deviation0.31556575
Coefficient of variation (CV)0.63488291
Kurtosis-1.2365648
Mean0.49704559
Median Absolute Deviation (MAD)0.27272727
Skewness0.0069345547
Sum2500.6364
Variance0.099581743
MonotonicityNot monotonic
2023-06-13T16:53:30.759285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.09090909091 449
8.9%
0.7272727273 428
8.5%
0 427
8.5%
0.9090909091 425
8.4%
0.8181818182 421
8.4%
0.2727272727 419
8.3%
0.5454545455 415
8.2%
0.3636363636 415
8.2%
0.1818181818 414
8.2%
1 411
8.2%
Other values (2) 807
16.0%
ValueCountFrequency (%)
0 427
8.5%
0.09090909091 449
8.9%
0.1818181818 414
8.2%
0.2727272727 419
8.3%
0.3636363636 415
8.2%
0.4545454545 405
8.1%
0.5454545455 415
8.2%
0.6363636364 402
8.0%
0.7272727273 428
8.5%
0.8181818182 421
8.4%
ValueCountFrequency (%)
1 411
8.2%
0.9090909091 425
8.4%
0.8181818182 421
8.4%
0.7272727273 428
8.5%
0.6363636364 402
8.0%
0.5454545455 415
8.2%
0.4545454545 405
8.1%
0.3636363636 415
8.2%
0.2727272727 419
8.3%
0.1818181818 414
8.2%

inflation_rate
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51006859
Minimum0
Maximum1
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:30.804764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18971411
Q10.41996513
median0.51950336
Q30.56655335
95-th percentile0.78452842
Maximum1
Range1
Interquartile range (IQR)0.14658822

Descriptive statistics

Standard deviation0.15351598
Coefficient of variation (CV)0.30097126
Kurtosis1.3165336
Mean0.51006859
Median Absolute Deviation (MAD)0.063172512
Skewness0.072921111
Sum2566.1551
Variance0.023567157
MonotonicityNot monotonic
2023-06-13T16:53:30.849131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.5195033609 943
18.7%
0.4563308489 907
18.0%
0.5665533455 745
14.8%
0.4199651268 692
13.8%
0.6446062616 501
10.0%
0.784528418 492
9.8%
0.3462908713 410
8.1%
0.1897141067 207
 
4.1%
0.0659911808 68
 
1.4%
1 55
 
1.1%
ValueCountFrequency (%)
0 11
 
0.2%
0.0659911808 68
 
1.4%
0.1897141067 207
 
4.1%
0.3462908713 410
8.1%
0.4199651268 692
13.8%
0.4563308489 907
18.0%
0.5195033609 943
18.7%
0.5665533455 745
14.8%
0.6446062616 501
10.0%
0.784528418 492
9.8%
ValueCountFrequency (%)
1 55
 
1.1%
0.784528418 492
9.8%
0.6446062616 501
10.0%
0.5665533455 745
14.8%
0.5195033609 943
18.7%
0.4563308489 907
18.0%
0.4199651268 692
13.8%
0.3462908713 410
8.1%
0.1897141067 207
 
4.1%
0.0659911808 68
 
1.4%

Average_income
Real number (ℝ)

Distinct71
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36262404
Minimum0
Maximum1
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:30.905554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.054956808
Q10.12699066
median0.23111007
Q30.52850679
95-th percentile0.95759535
Maximum1
Range1
Interquartile range (IQR)0.40151613

Descriptive statistics

Standard deviation0.31449904
Coefficient of variation (CV)0.86728678
Kurtosis-0.42799425
Mean0.36262404
Median Absolute Deviation (MAD)0.15379914
Skewness1.0004382
Sum1824.3615
Variance0.098909644
MonotonicityNot monotonic
2023-06-13T16:53:30.970166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9575953458 854
 
17.0%
0.3849092084 573
 
11.4%
0.5285067873 365
 
7.3%
0.1436093318 224
 
4.5%
0.252194864 186
 
3.7%
0.2033143327 164
 
3.3%
0.09402362344 132
 
2.6%
0.1705235941 131
 
2.6%
0.135828877 120
 
2.4%
0.1748369278 106
 
2.1%
Other values (61) 2176
43.3%
ValueCountFrequency (%)
0 11
 
0.2%
0.006675677264 11
 
0.2%
0.01306928366 29
0.6%
0.0200740436 11
 
0.2%
0.03033437151 21
0.4%
0.03698654287 29
0.6%
0.037703473 16
0.3%
0.04792854205 17
0.3%
0.04943292002 15
0.3%
0.05043192102 15
0.3%
ValueCountFrequency (%)
1 46
 
0.9%
0.9575953458 854
17.0%
0.7403655168 15
 
0.3%
0.6364929188 97
 
1.9%
0.6250337897 10
 
0.2%
0.5321031909 8
 
0.2%
0.5285067873 365
7.3%
0.3849092084 573
11.4%
0.3464535465 48
 
1.0%
0.344561321 67
 
1.3%

me_mln_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
3335 
1.0
1696 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15093
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3335
66.3%
1.0 1696
33.7%

Length

2023-06-13T16:53:31.023728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:31.074738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3335
66.3%
1.0 1696
33.7%

Most occurring characters

ValueCountFrequency (%)
0 8366
55.4%
. 5031
33.3%
1 1696
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10062
66.7%
Other Punctuation 5031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8366
83.1%
1 1696
 
16.9%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8366
55.4%
. 5031
33.3%
1 1696
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8366
55.4%
. 5031
33.3%
1 1696
 
11.2%

500k-mln_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
4338 
1.0
693 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15093
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4338
86.2%
1.0 693
 
13.8%

Length

2023-06-13T16:53:31.113756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:31.158867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4338
86.2%
1.0 693
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 9369
62.1%
. 5031
33.3%
1 693
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10062
66.7%
Other Punctuation 5031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9369
93.1%
1 693
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9369
62.1%
. 5031
33.3%
1 693
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9369
62.1%
. 5031
33.3%
1 693
 
4.6%

250k-500k_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
4454 
1.0
577 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15093
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4454
88.5%
1.0 577
 
11.5%

Length

2023-06-13T16:53:31.197809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:31.241912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4454
88.5%
1.0 577
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 9485
62.8%
. 5031
33.3%
1 577
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10062
66.7%
Other Punctuation 5031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9485
94.3%
1 577
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9485
62.8%
. 5031
33.3%
1 577
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9485
62.8%
. 5031
33.3%
1 577
 
3.8%

100k-250k_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
4291 
1.0
740 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15093
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4291
85.3%
1.0 740
 
14.7%

Length

2023-06-13T16:53:31.280216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:31.324156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4291
85.3%
1.0 740
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 9322
61.8%
. 5031
33.3%
1 740
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10062
66.7%
Other Punctuation 5031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9322
92.6%
1 740
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9322
61.8%
. 5031
33.3%
1 740
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9322
61.8%
. 5031
33.3%
1 740
 
4.9%

lt_100k_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
3706 
1.0
1325 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15093
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 3706
73.7%
1.0 1325
 
26.3%

Length

2023-06-13T16:53:31.362659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:31.406611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3706
73.7%
1.0 1325
 
26.3%

Most occurring characters

ValueCountFrequency (%)
0 8737
57.9%
. 5031
33.3%
1 1325
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10062
66.7%
Other Punctuation 5031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8737
86.8%
1 1325
 
13.2%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8737
57.9%
. 5031
33.3%
1 1325
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8737
57.9%
. 5031
33.3%
1 1325
 
8.8%

quarter
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
1296 
0.3333333333333333
1257 
1.0
1249 
0.6666666666666667
1229 

Length

Max length18
Median length3
Mean length10.412045
Min length3

Characters and Unicode

Total characters52383
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.3333333333333333
3rd row0.3333333333333333
4th row0.0
5th row0.3333333333333333

Common Values

ValueCountFrequency (%)
0.0 1296
25.8%
0.3333333333333333 1257
25.0%
1.0 1249
24.8%
0.6666666666666667 1229
24.4%

Length

2023-06-13T16:53:31.447571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:31.497993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1296
25.8%
0.3333333333333333 1257
25.0%
1.0 1249
24.8%
0.6666666666666667 1229
24.4%

Most occurring characters

ValueCountFrequency (%)
3 20112
38.4%
6 18435
35.2%
0 6327
 
12.1%
. 5031
 
9.6%
1 1249
 
2.4%
7 1229
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47352
90.4%
Other Punctuation 5031
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 20112
42.5%
6 18435
38.9%
0 6327
 
13.4%
1 1249
 
2.6%
7 1229
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52383
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 20112
38.4%
6 18435
35.2%
0 6327
 
12.1%
. 5031
 
9.6%
1 1249
 
2.4%
7 1229
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 20112
38.4%
6 18435
35.2%
0 6327
 
12.1%
. 5031
 
9.6%
1 1249
 
2.4%
7 1229
 
2.3%

apartments_lt_5lvl
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct249
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14269172
Minimum0
Maximum1
Zeros135
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:31.554425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003931848
Q10.02621232
median0.082568807
Q30.19003932
95-th percentile0.50196592
Maximum1
Range1
Interquartile range (IQR)0.163827

Descriptive statistics

Standard deviation0.17276116
Coefficient of variation (CV)1.2107301
Kurtosis5.2497685
Mean0.14269172
Median Absolute Deviation (MAD)0.065530799
Skewness2.1607228
Sum717.88204
Variance0.029846417
MonotonicityNot monotonic
2023-06-13T16:53:31.624204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
2.7%
0.006553079948 121
 
2.4%
0.0131061599 104
 
2.1%
0.01703800786 101
 
2.0%
0.003931847969 80
 
1.6%
0.007863695937 79
 
1.6%
0.02228047182 77
 
1.5%
0.02621231979 75
 
1.5%
0.03276539974 72
 
1.4%
0.01179554391 67
 
1.3%
Other values (239) 4120
81.9%
ValueCountFrequency (%)
0 135
2.7%
0.00131061599 49
 
1.0%
0.002621231979 51
 
1.0%
0.003931847969 80
1.6%
0.005242463958 21
 
0.4%
0.006553079948 121
2.4%
0.007863695937 79
1.6%
0.009174311927 30
 
0.6%
0.01048492792 38
 
0.8%
0.01179554391 67
1.3%
ValueCountFrequency (%)
1 7
0.1%
0.9475753604 9
0.2%
0.9370904325 7
0.1%
0.9082568807 6
0.1%
0.9030144168 9
0.2%
0.8610747051 11
0.2%
0.8597640891 8
0.2%
0.8309305374 8
0.2%
0.8034076016 9
0.2%
0.7824377457 9
0.2%

apartments_me_5lvl_lt_10lvl
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct406
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19325263
Minimum0
Maximum1
Zeros56
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:31.694675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0089342693
Q10.066368858
median0.16145501
Q30.28206765
95-th percentile0.49266114
Maximum1
Range1
Interquartile range (IQR)0.21569879

Descriptive statistics

Standard deviation0.1610714
Coefficient of variation (CV)0.83347586
Kurtosis2.4717031
Mean0.19325263
Median Absolute Deviation (MAD)0.10657307
Skewness1.2997873
Sum972.25399
Variance0.025943997
MonotonicityNot monotonic
2023-06-13T16:53:31.757254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
1.1%
0.05871091257 41
 
0.8%
0.04211869815 39
 
0.8%
0.03573707722 38
 
0.8%
0.03318442884 36
 
0.7%
0.008934269304 36
 
0.7%
0.2291001914 35
 
0.7%
0.02425015954 35
 
0.7%
0.1716656031 34
 
0.7%
0.04403318443 29
 
0.6%
Other values (396) 4652
92.5%
ValueCountFrequency (%)
0 56
1.1%
0.0006381620932 17
 
0.3%
0.001276324186 15
 
0.3%
0.00191448628 12
 
0.2%
0.002552648373 10
 
0.2%
0.003190810466 14
 
0.3%
0.003828972559 23
0.5%
0.004467134652 17
 
0.3%
0.005105296745 20
 
0.4%
0.005743458839 7
 
0.1%
ValueCountFrequency (%)
1 7
0.1%
0.9610721123 9
0.2%
0.9419272495 9
0.2%
0.8245054244 7
0.1%
0.7472878111 7
0.1%
0.6968730057 8
0.2%
0.6604977664 8
0.2%
0.6362476069 9
0.2%
0.6349712827 10
0.2%
0.6285896618 9
0.2%

apartments_me_10lvl_lt_15lvl
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct204
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13289885
Minimum0
Maximum1
Zeros492
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:31.820544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.016697588
median0.089053803
Q30.20871985
95-th percentile0.40630798
Maximum1
Range1
Interquartile range (IQR)0.19202226

Descriptive statistics

Standard deviation0.1438039
Coefficient of variation (CV)1.0820553
Kurtosis3.8884414
Mean0.13289885
Median Absolute Deviation (MAD)0.079777365
Skewness1.6464557
Sum668.6141
Variance0.020679563
MonotonicityNot monotonic
2023-06-13T16:53:31.884539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 492
 
9.8%
0.00185528757 140
 
2.8%
0.007421150278 118
 
2.3%
0.009276437848 115
 
2.3%
0.03896103896 110
 
2.2%
0.01669758813 99
 
2.0%
0.005565862709 87
 
1.7%
0.03339517625 82
 
1.6%
0.01484230056 78
 
1.6%
0.003710575139 73
 
1.5%
Other values (194) 3637
72.3%
ValueCountFrequency (%)
0 492
9.8%
0.00185528757 140
 
2.8%
0.003710575139 73
 
1.5%
0.005565862709 87
 
1.7%
0.007421150278 118
 
2.3%
0.009276437848 115
 
2.3%
0.01113172542 53
 
1.1%
0.01298701299 57
 
1.1%
0.01484230056 78
 
1.6%
0.01669758813 99
 
2.0%
ValueCountFrequency (%)
1 9
 
0.2%
0.8682745826 6
 
0.1%
0.7142857143 9
 
0.2%
0.680890538 8
 
0.2%
0.6363636364 7
 
0.1%
0.6085343228 10
 
0.2%
0.6029684601 8
 
0.2%
0.586270872 9
 
0.2%
0.5584415584 28
0.6%
0.5435992579 8
 
0.2%

apartments_me_15lvl
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct189
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15856938
Minimum0
Maximum1
Zeros1127
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:31.949271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0027932961
median0.067039106
Q30.24581006
95-th percentile0.59497207
Maximum1
Range1
Interquartile range (IQR)0.24301676

Descriptive statistics

Standard deviation0.20403757
Coefficient of variation (CV)1.28674
Kurtosis2.0459087
Mean0.15856938
Median Absolute Deviation (MAD)0.067039106
Skewness1.5676862
Sum797.76257
Variance0.041631331
MonotonicityNot monotonic
2023-06-13T16:53:32.011528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1127
 
22.4%
0.002793296089 166
 
3.3%
0.01396648045 126
 
2.5%
0.01955307263 126
 
2.5%
0.008379888268 114
 
2.3%
0.005586592179 105
 
2.1%
0.01117318436 76
 
1.5%
0.01675977654 74
 
1.5%
0.03351955307 69
 
1.4%
0.09497206704 65
 
1.3%
Other values (179) 2983
59.3%
ValueCountFrequency (%)
0 1127
22.4%
0.002793296089 166
 
3.3%
0.005586592179 105
 
2.1%
0.008379888268 114
 
2.3%
0.01117318436 76
 
1.5%
0.01396648045 126
 
2.5%
0.01675977654 74
 
1.5%
0.01955307263 126
 
2.5%
0.02234636872 60
 
1.2%
0.0251396648 37
 
0.7%
ValueCountFrequency (%)
1 10
0.2%
0.9413407821 8
0.2%
0.9357541899 7
0.1%
0.8882681564 11
0.2%
0.8687150838 7
0.1%
0.8240223464 8
0.2%
0.8156424581 9
0.2%
0.8100558659 4
 
0.1%
0.8072625698 9
0.2%
0.7988826816 9
0.2%

dormitory
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1477376
Minimum0
Maximum1
Zeros926
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.074063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.017857143
median0.071428571
Q30.21428571
95-th percentile0.58035714
Maximum1
Range1
Interquartile range (IQR)0.19642857

Descriptive statistics

Standard deviation0.18316223
Coefficient of variation (CV)1.2397808
Kurtosis3.8652633
Mean0.1477376
Median Absolute Deviation (MAD)0.071428571
Skewness1.911279
Sum743.26786
Variance0.033548404
MonotonicityNot monotonic
2023-06-13T16:53:32.136962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 926
18.4%
0.01785714286 584
 
11.6%
0.03571428571 512
 
10.2%
0.05357142857 278
 
5.5%
0.1071428571 236
 
4.7%
0.07142857143 232
 
4.6%
0.08928571429 201
 
4.0%
0.1607142857 173
 
3.4%
0.1785714286 173
 
3.4%
0.1428571429 171
 
3.4%
Other values (39) 1545
30.7%
ValueCountFrequency (%)
0 926
18.4%
0.01785714286 584
11.6%
0.03571428571 512
10.2%
0.05357142857 278
 
5.5%
0.07142857143 232
 
4.6%
0.08928571429 201
 
4.0%
0.1071428571 236
 
4.7%
0.125 155
 
3.1%
0.1428571429 171
 
3.4%
0.1607142857 173
 
3.4%
ValueCountFrequency (%)
1 9
0.2%
0.9821428571 8
0.2%
0.9464285714 9
0.2%
0.8571428571 7
 
0.1%
0.8392857143 7
 
0.1%
0.8214285714 18
0.4%
0.8035714286 17
0.3%
0.7678571429 10
0.2%
0.75 6
 
0.1%
0.7321428571 10
0.2%

hotel
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047748785
Minimum0
Maximum1
Zeros1733
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.193057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.017241379
Q30.068965517
95-th percentile0.17241379
Maximum1
Range1
Interquartile range (IQR)0.068965517

Descriptive statistics

Standard deviation0.078444119
Coefficient of variation (CV)1.6428506
Kurtosis49.099638
Mean0.047748785
Median Absolute Deviation (MAD)0.017241379
Skewness5.3164643
Sum240.22414
Variance0.0061534797
MonotonicityNot monotonic
2023-06-13T16:53:32.243188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1733
34.4%
0.01724137931 1027
20.4%
0.03448275862 510
 
10.1%
0.05172413793 414
 
8.2%
0.06896551724 295
 
5.9%
0.08620689655 275
 
5.5%
0.1034482759 156
 
3.1%
0.1379310345 133
 
2.6%
0.1551724138 109
 
2.2%
0.1206896552 98
 
1.9%
Other values (13) 281
 
5.6%
ValueCountFrequency (%)
0 1733
34.4%
0.01724137931 1027
20.4%
0.03448275862 510
 
10.1%
0.05172413793 414
 
8.2%
0.06896551724 295
 
5.9%
0.08620689655 275
 
5.5%
0.1034482759 156
 
3.1%
0.1206896552 98
 
1.9%
0.1379310345 133
 
2.6%
0.1551724138 109
 
2.2%
ValueCountFrequency (%)
1 9
 
0.2%
0.724137931 9
 
0.2%
0.4482758621 6
 
0.1%
0.4137931034 7
 
0.1%
0.3275862069 9
 
0.2%
0.2931034483 9
 
0.2%
0.275862069 25
0.5%
0.2586206897 17
 
0.3%
0.2413793103 18
0.4%
0.224137931 44
0.9%

residential
Real number (ℝ)

Distinct143
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01459861
Minimum0
Maximum1
Zeros679
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.301815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00036023055
median0.0016210375
Q30.0064841499
95-th percentile0.076008646
Maximum1
Range1
Interquartile range (IQR)0.0061239193

Descriptive statistics

Standard deviation0.059902515
Coefficient of variation (CV)4.1033028
Kurtosis150.13099
Mean0.01459861
Median Absolute Deviation (MAD)0.0016210375
Skewness10.785919
Sum73.445605
Variance0.0035883113
MonotonicityNot monotonic
2023-06-13T16:53:32.363885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 679
 
13.5%
0.0001801152738 511
 
10.2%
0.0003602305476 336
 
6.7%
0.0005403458213 262
 
5.2%
0.0007204610951 168
 
3.3%
0.0009005763689 155
 
3.1%
0.001080691643 140
 
2.8%
0.001801152738 121
 
2.4%
0.00144092219 115
 
2.3%
0.002521613833 96
 
1.9%
Other values (133) 2448
48.7%
ValueCountFrequency (%)
0 679
13.5%
0.0001801152738 511
10.2%
0.0003602305476 336
6.7%
0.0005403458213 262
 
5.2%
0.0007204610951 168
 
3.3%
0.0009005763689 155
 
3.1%
0.001080691643 140
 
2.8%
0.001260806916 95
 
1.9%
0.00144092219 115
 
2.3%
0.001621037464 59
 
1.2%
ValueCountFrequency (%)
1 9
0.2%
0.626981268 6
0.1%
0.4364193084 7
0.1%
0.4191282421 11
0.2%
0.2053314121 9
0.2%
0.1941642651 8
0.2%
0.1905619597 11
0.2%
0.1680475504 8
0.2%
0.1503962536 8
0.2%
0.1475144092 10
0.2%

commercial
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct142
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050226018
Minimum0
Maximum1
Zeros54
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.426376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0021505376
Q10.0096774194
median0.021505376
Q30.050537634
95-th percentile0.1827957
Maximum1
Range1
Interquartile range (IQR)0.040860215

Descriptive statistics

Standard deviation0.094355958
Coefficient of variation (CV)1.8786271
Kurtosis37.212327
Mean0.050226018
Median Absolute Deviation (MAD)0.013978495
Skewness5.3446924
Sum252.6871
Variance0.0089030469
MonotonicityNot monotonic
2023-06-13T16:53:32.488542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002150537634 183
 
3.6%
0.00752688172 178
 
3.5%
0.009677419355 157
 
3.1%
0.01182795699 157
 
3.1%
0.006451612903 153
 
3.0%
0.01612903226 147
 
2.9%
0.01720430108 146
 
2.9%
0.001075268817 145
 
2.9%
0.01935483871 144
 
2.9%
0.003225806452 142
 
2.8%
Other values (132) 3479
69.2%
ValueCountFrequency (%)
0 54
 
1.1%
0.001075268817 145
2.9%
0.002150537634 183
3.6%
0.003225806452 142
2.8%
0.004301075269 134
2.7%
0.005376344086 82
1.6%
0.006451612903 153
3.0%
0.00752688172 178
3.5%
0.008602150538 62
 
1.2%
0.009677419355 157
3.1%
ValueCountFrequency (%)
1 8
0.2%
0.8397849462 9
0.2%
0.723655914 9
0.2%
0.6849462366 11
0.2%
0.6494623656 8
0.2%
0.4913978495 9
0.2%
0.435483871 8
0.2%
0.423655914 8
0.2%
0.4107526882 8
0.2%
0.3967741935 9
0.2%

office
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.053646298
Minimum0
Maximum1
Zeros599
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.551282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0040567951
median0.014198783
Q30.048681542
95-th percentile0.23935091
Maximum1
Range1
Interquartile range (IQR)0.044624746

Descriptive statistics

Standard deviation0.10407526
Coefficient of variation (CV)1.9400268
Kurtosis22.832297
Mean0.053646298
Median Absolute Deviation (MAD)0.012170385
Skewness4.1538243
Sum269.89452
Variance0.010831659
MonotonicityNot monotonic
2023-06-13T16:53:32.614215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 599
 
11.9%
0.002028397566 366
 
7.3%
0.004056795132 355
 
7.1%
0.008113590264 339
 
6.7%
0.01014198783 247
 
4.9%
0.006085192698 231
 
4.6%
0.01419878296 194
 
3.9%
0.0121703854 190
 
3.8%
0.01825557809 152
 
3.0%
0.01622718053 133
 
2.6%
Other values (104) 2225
44.2%
ValueCountFrequency (%)
0 599
11.9%
0.002028397566 366
7.3%
0.004056795132 355
7.1%
0.006085192698 231
 
4.6%
0.008113590264 339
6.7%
0.01014198783 247
4.9%
0.0121703854 190
 
3.8%
0.01419878296 194
 
3.9%
0.01622718053 133
 
2.6%
0.01825557809 152
 
3.0%
ValueCountFrequency (%)
1 8
0.2%
0.7687626775 11
0.2%
0.6896551724 7
0.1%
0.6531440162 6
 
0.1%
0.6146044625 16
0.3%
0.4949290061 6
 
0.1%
0.4888438134 8
0.2%
0.4847870183 10
0.2%
0.476673428 10
0.2%
0.4584178499 10
0.2%

retail
Real number (ℝ)

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085208048
Minimum0
Maximum1
Zeros32
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.677124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0056338028
Q10.026760563
median0.057746479
Q30.11126761
95-th percentile0.26619718
Maximum1
Range1
Interquartile range (IQR)0.084507042

Descriptive statistics

Standard deviation0.09444085
Coefficient of variation (CV)1.108356
Kurtosis18.717977
Mean0.085208048
Median Absolute Deviation (MAD)0.035211268
Skewness3.3006276
Sum428.68169
Variance0.0089190742
MonotonicityNot monotonic
2023-06-13T16:53:32.739226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05211267606 105
 
2.1%
0.007042253521 100
 
2.0%
0.03661971831 92
 
1.8%
0.005633802817 86
 
1.7%
0.03098591549 85
 
1.7%
0.05070422535 84
 
1.7%
0.02253521127 84
 
1.7%
0.02535211268 82
 
1.6%
0.01690140845 78
 
1.6%
0.01549295775 77
 
1.5%
Other values (160) 4158
82.6%
ValueCountFrequency (%)
0 32
 
0.6%
0.001408450704 40
 
0.8%
0.002816901408 50
1.0%
0.004225352113 75
1.5%
0.005633802817 86
1.7%
0.007042253521 100
2.0%
0.008450704225 37
 
0.7%
0.00985915493 40
 
0.8%
0.01126760563 72
1.4%
0.01267605634 46
0.9%
ValueCountFrequency (%)
1 8
0.2%
0.6281690141 8
0.2%
0.5690140845 8
0.2%
0.5633802817 11
0.2%
0.4281690141 9
0.2%
0.4267605634 10
0.2%
0.414084507 10
0.2%
0.4112676056 9
0.2%
0.4042253521 11
0.2%
0.3943661972 7
0.1%

civic
Real number (ℝ)

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048723555
Minimum0
Maximum1
Zeros2785
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.794268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.051282051
95-th percentile0.23076923
Maximum1
Range1
Interquartile range (IQR)0.051282051

Descriptive statistics

Standard deviation0.11421325
Coefficient of variation (CV)2.3441074
Kurtosis25.165586
Mean0.048723555
Median Absolute Deviation (MAD)0
Skewness4.4974007
Sum245.12821
Variance0.013044666
MonotonicityNot monotonic
2023-06-13T16:53:32.845466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2785
55.4%
0.02564102564 803
 
16.0%
0.05128205128 509
 
10.1%
0.07692307692 251
 
5.0%
0.1025641026 135
 
2.7%
0.1282051282 105
 
2.1%
0.1538461538 71
 
1.4%
0.1794871795 60
 
1.2%
0.2307692308 41
 
0.8%
0.2820512821 32
 
0.6%
Other values (14) 239
 
4.8%
ValueCountFrequency (%)
0 2785
55.4%
0.02564102564 803
 
16.0%
0.05128205128 509
 
10.1%
0.07692307692 251
 
5.0%
0.1025641026 135
 
2.7%
0.1282051282 105
 
2.1%
0.1538461538 71
 
1.4%
0.1794871795 60
 
1.2%
0.2051282051 24
 
0.5%
0.2307692308 41
 
0.8%
ValueCountFrequency (%)
1 16
0.3%
0.7692307692 9
 
0.2%
0.7179487179 16
0.3%
0.6923076923 9
 
0.2%
0.5384615385 30
0.6%
0.4871794872 15
0.3%
0.4615384615 17
0.3%
0.4358974359 8
 
0.2%
0.3846153846 9
 
0.2%
0.358974359 26
0.5%

college
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08361687
Minimum0
Maximum1
Zeros1745
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:32.896856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.029411765
Q30.11764706
95-th percentile0.32352941
Maximum1
Range1
Interquartile range (IQR)0.11764706

Descriptive statistics

Standard deviation0.11360283
Coefficient of variation (CV)1.3586114
Kurtosis9.5688833
Mean0.08361687
Median Absolute Deviation (MAD)0.029411765
Skewness2.427786
Sum420.67647
Variance0.012905603
MonotonicityNot monotonic
2023-06-13T16:53:32.945461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1745
34.7%
0.02941176471 805
16.0%
0.05882352941 640
 
12.7%
0.08823529412 427
 
8.5%
0.1764705882 266
 
5.3%
0.1176470588 262
 
5.2%
0.1470588235 222
 
4.4%
0.2058823529 113
 
2.2%
0.2352941176 107
 
2.1%
0.2647058824 103
 
2.0%
Other values (11) 341
 
6.8%
ValueCountFrequency (%)
0 1745
34.7%
0.02941176471 805
16.0%
0.05882352941 640
 
12.7%
0.08823529412 427
 
8.5%
0.1176470588 262
 
5.2%
0.1470588235 222
 
4.4%
0.1764705882 266
 
5.3%
0.2058823529 113
 
2.2%
0.2352941176 107
 
2.1%
0.2647058824 103
 
2.0%
ValueCountFrequency (%)
1 9
 
0.2%
0.5588235294 20
 
0.4%
0.5294117647 14
 
0.3%
0.5 15
 
0.3%
0.4705882353 17
 
0.3%
0.4411764706 6
 
0.1%
0.4117647059 21
 
0.4%
0.3823529412 51
1.0%
0.3529411765 81
1.6%
0.3235294118 55
1.1%

school
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18461808
Minimum0
Maximum1
Zeros59
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.003098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.025423729
Q10.076271186
median0.16101695
Q30.27118644
95-th percentile0.43220339
Maximum1
Range1
Interquartile range (IQR)0.19491525

Descriptive statistics

Standard deviation0.13923516
Coefficient of variation (CV)0.75417943
Kurtosis2.1795598
Mean0.18461808
Median Absolute Deviation (MAD)0.093220339
Skewness1.1828173
Sum928.81356
Variance0.019386429
MonotonicityNot monotonic
2023-06-13T16:53:33.066566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07627118644 199
 
4.0%
0.04237288136 196
 
3.9%
0.09322033898 194
 
3.9%
0.06779661017 188
 
3.7%
0.2033898305 173
 
3.4%
0.05084745763 164
 
3.3%
0.0593220339 162
 
3.2%
0.02542372881 157
 
3.1%
0.08474576271 150
 
3.0%
0.1355932203 142
 
2.8%
Other values (59) 3306
65.7%
ValueCountFrequency (%)
0 59
 
1.2%
0.008474576271 83
1.6%
0.01694915254 105
2.1%
0.02542372881 157
3.1%
0.03389830508 118
2.3%
0.04237288136 196
3.9%
0.05084745763 164
3.3%
0.0593220339 162
3.2%
0.06779661017 188
3.7%
0.07627118644 199
4.0%
ValueCountFrequency (%)
1 8
 
0.2%
0.7966101695 6
 
0.1%
0.6440677966 10
0.2%
0.6186440678 21
0.4%
0.6016949153 7
 
0.1%
0.5847457627 6
 
0.1%
0.5762711864 23
0.5%
0.5677966102 9
 
0.2%
0.5338983051 22
0.4%
0.5254237288 23
0.5%

transportation
Real number (ℝ)

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038331575
Minimum0
Maximum1
Zeros3155
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.122060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.025641026
95-th percentile0.20512821
Maximum1
Range1
Interquartile range (IQR)0.025641026

Descriptive statistics

Standard deviation0.094405609
Coefficient of variation (CV)2.462868
Kurtosis31.131939
Mean0.038331575
Median Absolute Deviation (MAD)0
Skewness4.7588496
Sum192.84615
Variance0.0089124191
MonotonicityNot monotonic
2023-06-13T16:53:33.170160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 3155
62.7%
0.02564102564 779
 
15.5%
0.05128205128 346
 
6.9%
0.1025641026 156
 
3.1%
0.1794871795 119
 
2.4%
0.07692307692 114
 
2.3%
0.1538461538 58
 
1.2%
0.1282051282 41
 
0.8%
0.2051282051 38
 
0.8%
0.2564102564 36
 
0.7%
Other values (12) 189
 
3.8%
ValueCountFrequency (%)
0 3155
62.7%
0.02564102564 779
 
15.5%
0.05128205128 346
 
6.9%
0.07692307692 114
 
2.3%
0.1025641026 156
 
3.1%
0.1282051282 41
 
0.8%
0.1538461538 58
 
1.2%
0.1794871795 119
 
2.4%
0.2051282051 38
 
0.8%
0.2307692308 23
 
0.5%
ValueCountFrequency (%)
1 9
 
0.2%
0.6923076923 7
 
0.1%
0.6666666667 9
 
0.2%
0.641025641 9
 
0.2%
0.5128205128 17
0.3%
0.4102564103 18
0.4%
0.3846153846 8
 
0.2%
0.358974359 6
 
0.1%
0.3333333333 34
0.7%
0.3076923077 15
0.3%

university
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07292343
Minimum0
Maximum1
Zeros1790
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.227137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.022222222
Q30.088888889
95-th percentile0.34444444
Maximum1
Range1
Interquartile range (IQR)0.088888889

Descriptive statistics

Standard deviation0.1283518
Coefficient of variation (CV)1.76009
Kurtosis11.175269
Mean0.07292343
Median Absolute Deviation (MAD)0.022222222
Skewness2.9869803
Sum366.87778
Variance0.016474185
MonotonicityNot monotonic
2023-06-13T16:53:33.289504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1790
35.6%
0.01111111111 675
 
13.4%
0.02222222222 446
 
8.9%
0.03333333333 274
 
5.4%
0.04444444444 199
 
4.0%
0.07777777778 130
 
2.6%
0.06666666667 123
 
2.4%
0.05555555556 106
 
2.1%
0.1666666667 105
 
2.1%
0.1333333333 97
 
1.9%
Other values (41) 1086
21.6%
ValueCountFrequency (%)
0 1790
35.6%
0.01111111111 675
 
13.4%
0.02222222222 446
 
8.9%
0.03333333333 274
 
5.4%
0.04444444444 199
 
4.0%
0.05555555556 106
 
2.1%
0.06666666667 123
 
2.4%
0.07777777778 130
 
2.6%
0.08888888889 84
 
1.7%
0.1 77
 
1.5%
ValueCountFrequency (%)
1 9
0.2%
0.7555555556 7
 
0.1%
0.7222222222 19
0.4%
0.6444444444 20
0.4%
0.6111111111 17
0.3%
0.6 9
0.2%
0.5888888889 8
 
0.2%
0.5 9
0.2%
0.4888888889 8
 
0.2%
0.4777777778 9
0.2%

stadium
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032171962
Minimum0
Maximum1
Zeros4431
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.340066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.28571429
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10824425
Coefficient of variation (CV)3.3645524
Kurtosis25.090859
Mean0.032171962
Median Absolute Deviation (MAD)0
Skewness4.5483376
Sum161.85714
Variance0.011716818
MonotonicityNot monotonic
2023-06-13T16:53:33.380882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4431
88.1%
0.1428571429 347
 
6.9%
0.4285714286 136
 
2.7%
0.2857142857 74
 
1.5%
0.7142857143 25
 
0.5%
1 11
 
0.2%
0.5714285714 7
 
0.1%
ValueCountFrequency (%)
0 4431
88.1%
0.1428571429 347
 
6.9%
0.2857142857 74
 
1.5%
0.4285714286 136
 
2.7%
0.5714285714 7
 
0.1%
0.7142857143 25
 
0.5%
1 11
 
0.2%
ValueCountFrequency (%)
1 11
 
0.2%
0.7142857143 25
 
0.5%
0.5714285714 7
 
0.1%
0.4285714286 136
 
2.7%
0.2857142857 74
 
1.5%
0.1428571429 347
 
6.9%
0 4431
88.1%

sports_hall
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024821109
Minimum0
Maximum1
Zeros4435
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.422441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.125
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.089186559
Coefficient of variation (CV)3.5931738
Kurtosis42.970087
Mean0.024821109
Median Absolute Deviation (MAD)0
Skewness5.7484719
Sum124.875
Variance0.0079542423
MonotonicityNot monotonic
2023-06-13T16:53:33.464488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4435
88.2%
0.125 418
 
8.3%
0.25 69
 
1.4%
0.375 66
 
1.3%
0.625 19
 
0.4%
1 9
 
0.2%
0.75 9
 
0.2%
0.5 6
 
0.1%
ValueCountFrequency (%)
0 4435
88.2%
0.125 418
 
8.3%
0.25 69
 
1.4%
0.375 66
 
1.3%
0.5 6
 
0.1%
0.625 19
 
0.4%
0.75 9
 
0.2%
1 9
 
0.2%
ValueCountFrequency (%)
1 9
 
0.2%
0.75 9
 
0.2%
0.625 19
 
0.4%
0.5 6
 
0.1%
0.375 66
 
1.3%
0.25 69
 
1.4%
0.125 418
 
8.3%
0 4435
88.2%

pavilion
Real number (ℝ)

Distinct33
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0083371005
Minimum0
Maximum1
Zeros4029
Zeros (%)80.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.513888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.029498525
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.056433912
Coefficient of variation (CV)6.7690094
Kurtosis219.44817
Mean0.0083371005
Median Absolute Deviation (MAD)0
Skewness13.838603
Sum41.943953
Variance0.0031847864
MonotonicityNot monotonic
2023-06-13T16:53:33.571352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 4029
80.1%
0.005899705015 226
 
4.5%
0.002949852507 185
 
3.7%
0.008849557522 93
 
1.8%
0.01179941003 74
 
1.5%
0.02064896755 51
 
1.0%
0.01769911504 46
 
0.9%
0.01474926254 33
 
0.7%
0.02949852507 27
 
0.5%
0.02359882006 27
 
0.5%
Other values (23) 240
 
4.8%
ValueCountFrequency (%)
0 4029
80.1%
0.002949852507 185
 
3.7%
0.005899705015 226
 
4.5%
0.008849557522 93
 
1.8%
0.01179941003 74
 
1.5%
0.01474926254 33
 
0.7%
0.01769911504 46
 
0.9%
0.02064896755 51
 
1.0%
0.02359882006 27
 
0.5%
0.02654867257 14
 
0.3%
ValueCountFrequency (%)
1 9
0.2%
0.8377581121 5
0.1%
0.3657817109 10
0.2%
0.3244837758 7
0.1%
0.209439528 8
0.2%
0.2064896755 6
0.1%
0.1533923304 10
0.2%
0.1386430678 8
0.2%
0.1120943953 9
0.2%
0.1091445428 9
0.2%

bar
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04481106
Minimum0
Maximum1
Zeros3666
Zeros (%)72.9%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.618989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.11111111
95-th percentile0.22222222
Maximum1
Range1
Interquartile range (IQR)0.11111111

Descriptive statistics

Standard deviation0.091601867
Coefficient of variation (CV)2.0441799
Kurtosis22.233045
Mean0.04481106
Median Absolute Deviation (MAD)0
Skewness3.5262958
Sum225.44444
Variance0.0083909021
MonotonicityNot monotonic
2023-06-13T16:53:33.659840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3666
72.9%
0.1111111111 912
 
18.1%
0.2222222222 342
 
6.8%
0.3333333333 60
 
1.2%
0.4444444444 34
 
0.7%
0.5555555556 9
 
0.2%
1 8
 
0.2%
ValueCountFrequency (%)
0 3666
72.9%
0.1111111111 912
 
18.1%
0.2222222222 342
 
6.8%
0.3333333333 60
 
1.2%
0.4444444444 34
 
0.7%
0.5555555556 9
 
0.2%
1 8
 
0.2%
ValueCountFrequency (%)
1 8
 
0.2%
0.5555555556 9
 
0.2%
0.4444444444 34
 
0.7%
0.3333333333 60
 
1.2%
0.2222222222 342
 
6.8%
0.1111111111 912
 
18.1%
0 3666
72.9%

fast_food
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10270516
Minimum0
Maximum1
Zeros1229
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.707357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.032258065
median0.064516129
Q30.12903226
95-th percentile0.35483871
Maximum1
Range1
Interquartile range (IQR)0.096774194

Descriptive statistics

Standard deviation0.14007967
Coefficient of variation (CV)1.3639009
Kurtosis11.109492
Mean0.10270516
Median Absolute Deviation (MAD)0.064516129
Skewness2.9618559
Sum516.70968
Variance0.019622314
MonotonicityNot monotonic
2023-06-13T16:53:33.758747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 1229
24.4%
0.06451612903 952
18.9%
0.03225806452 855
17.0%
0.09677419355 551
11.0%
0.1290322581 404
 
8.0%
0.1612903226 206
 
4.1%
0.1935483871 179
 
3.6%
0.2258064516 142
 
2.8%
0.2580645161 119
 
2.4%
0.3225806452 71
 
1.4%
Other values (17) 323
 
6.4%
ValueCountFrequency (%)
0 1229
24.4%
0.03225806452 855
17.0%
0.06451612903 952
18.9%
0.09677419355 551
11.0%
0.1290322581 404
 
8.0%
0.1612903226 206
 
4.1%
0.1935483871 179
 
3.6%
0.2258064516 142
 
2.8%
0.2580645161 119
 
2.4%
0.2903225806 50
 
1.0%
ValueCountFrequency (%)
1 8
 
0.2%
0.8709677419 14
0.3%
0.8387096774 20
0.4%
0.8064516129 11
0.2%
0.7419354839 10
0.2%
0.6774193548 10
0.2%
0.6451612903 19
0.4%
0.6129032258 13
0.3%
0.5806451613 14
0.3%
0.5483870968 19
0.4%

cafe
Real number (ℝ)

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13852749
Minimum0
Maximum1
Zeros517
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:33.812182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.045454545
median0.090909091
Q30.18181818
95-th percentile0.48863636
Maximum1
Range1
Interquartile range (IQR)0.13636364

Descriptive statistics

Standard deviation0.15189146
Coefficient of variation (CV)1.0964716
Kurtosis5.172341
Mean0.13852749
Median Absolute Deviation (MAD)0.068181818
Skewness2.0909689
Sum696.93182
Variance0.023071015
MonotonicityNot monotonic
2023-06-13T16:53:33.867654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.02272727273 641
12.7%
0.06818181818 569
11.3%
0.09090909091 530
10.5%
0 517
10.3%
0.04545454545 501
10.0%
0.1136363636 370
7.4%
0.1363636364 361
 
7.2%
0.1590909091 279
 
5.5%
0.2045454545 176
 
3.5%
0.1818181818 170
 
3.4%
Other values (24) 917
18.2%
ValueCountFrequency (%)
0 517
10.3%
0.02272727273 641
12.7%
0.04545454545 501
10.0%
0.06818181818 569
11.3%
0.09090909091 530
10.5%
0.1136363636 370
7.4%
0.1363636364 361
7.2%
0.1590909091 279
5.5%
0.1818181818 170
 
3.4%
0.2045454545 176
 
3.5%
ValueCountFrequency (%)
1 11
 
0.2%
0.9318181818 8
 
0.2%
0.7727272727 8
 
0.2%
0.6818181818 15
 
0.3%
0.6590909091 11
 
0.2%
0.6363636364 18
 
0.4%
0.6136363636 45
0.9%
0.5909090909 16
 
0.3%
0.5681818182 10
 
0.2%
0.5454545455 50
1.0%

food_court
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
4366 
0.3333333333333333
543 
0.6666666666666666
 
105
1.0
 
17

Length

Max length18
Median length3
Mean length4.9320215
Min length3

Characters and Unicode

Total characters24813
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4366
86.8%
0.3333333333333333 543
 
10.8%
0.6666666666666666 105
 
2.1%
1.0 17
 
0.3%

Length

2023-06-13T16:53:33.920496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:33.969755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4366
86.8%
0.3333333333333333 543
 
10.8%
0.6666666666666666 105
 
2.1%
1.0 17
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 9397
37.9%
3 8688
35.0%
. 5031
20.3%
6 1680
 
6.8%
1 17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19782
79.7%
Other Punctuation 5031
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9397
47.5%
3 8688
43.9%
6 1680
 
8.5%
1 17
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9397
37.9%
3 8688
35.0%
. 5031
20.3%
6 1680
 
6.8%
1 17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9397
37.9%
3 8688
35.0%
. 5031
20.3%
6 1680
 
6.8%
1 17
 
0.1%

restaurant
Real number (ℝ)

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15122491
Minimum0
Maximum1
Zeros1365
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:34.013858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.125
Q30.1875
95-th percentile0.5
Maximum1
Range1
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.17786988
Coefficient of variation (CV)1.1761944
Kurtosis4.2982615
Mean0.15122491
Median Absolute Deviation (MAD)0.125
Skewness1.9289883
Sum760.8125
Variance0.031637695
MonotonicityNot monotonic
2023-06-13T16:53:34.061981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 1365
27.1%
0.0625 1096
21.8%
0.125 851
16.9%
0.1875 550
10.9%
0.25 309
 
6.1%
0.3125 229
 
4.6%
0.375 212
 
4.2%
0.4375 104
 
2.1%
0.5 94
 
1.9%
0.625 48
 
1.0%
Other values (7) 173
 
3.4%
ValueCountFrequency (%)
0 1365
27.1%
0.0625 1096
21.8%
0.125 851
16.9%
0.1875 550
10.9%
0.25 309
 
6.1%
0.3125 229
 
4.6%
0.375 212
 
4.2%
0.4375 104
 
2.1%
0.5 94
 
1.9%
0.5625 37
 
0.7%
ValueCountFrequency (%)
1 11
 
0.2%
0.9375 7
 
0.1%
0.875 30
 
0.6%
0.8125 42
0.8%
0.75 26
 
0.5%
0.6875 20
 
0.4%
0.625 48
1.0%
0.5625 37
 
0.7%
0.5 94
1.9%
0.4375 104
2.1%

cinema
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097097992
Minimum0
Maximum1
Zeros3062
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:34.106971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.16666667
95-th percentile0.33333333
Maximum1
Range1
Interquartile range (IQR)0.16666667

Descriptive statistics

Standard deviation0.15344211
Coefficient of variation (CV)1.580281
Kurtosis6.0422906
Mean0.097097992
Median Absolute Deviation (MAD)0
Skewness2.172687
Sum488.5
Variance0.023544481
MonotonicityNot monotonic
2023-06-13T16:53:34.149071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3062
60.9%
0.1666666667 1367
27.2%
0.3333333333 385
 
7.7%
0.5 120
 
2.4%
0.6666666667 60
 
1.2%
0.8333333333 28
 
0.6%
1 9
 
0.2%
ValueCountFrequency (%)
0 3062
60.9%
0.1666666667 1367
27.2%
0.3333333333 385
 
7.7%
0.5 120
 
2.4%
0.6666666667 60
 
1.2%
0.8333333333 28
 
0.6%
1 9
 
0.2%
ValueCountFrequency (%)
1 9
 
0.2%
0.8333333333 28
 
0.6%
0.6666666667 60
 
1.2%
0.5 120
 
2.4%
0.3333333333 385
 
7.7%
0.1666666667 1367
27.2%
0 3062
60.9%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0.0
4919 
0.5
 
93
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15093
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4919
97.8%
0.5 93
 
1.8%
1.0 19
 
0.4%

Length

2023-06-13T16:53:34.194541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.239551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4919
97.8%
0.5 93
 
1.8%
1.0 19
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 9950
65.9%
. 5031
33.3%
5 93
 
0.6%
1 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10062
66.7%
Other Punctuation 5031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9950
98.9%
5 93
 
0.9%
1 19
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 5031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9950
65.9%
. 5031
33.3%
5 93
 
0.6%
1 19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9950
65.9%
. 5031
33.3%
5 93
 
0.6%
1 19
 
0.1%

leisure
Real number (ℝ)

Distinct106
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11362199
Minimum0
Maximum1
Zeros30
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:34.288223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.016528926
Q10.037190083
median0.070247934
Q30.13636364
95-th percentile0.38842975
Maximum1
Range1
Interquartile range (IQR)0.099173554

Descriptive statistics

Standard deviation0.12692362
Coefficient of variation (CV)1.1170691
Kurtosis9.6053449
Mean0.11362199
Median Absolute Deviation (MAD)0.037190083
Skewness2.724781
Sum571.63223
Variance0.016109605
MonotonicityNot monotonic
2023-06-13T16:53:34.350724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03305785124 253
 
5.0%
0.02892561983 224
 
4.5%
0.04132231405 204
 
4.1%
0.04545454545 199
 
4.0%
0.03719008264 188
 
3.7%
0.01652892562 179
 
3.6%
0.06611570248 177
 
3.5%
0.06198347107 159
 
3.2%
0.0867768595 152
 
3.0%
0.02066115702 144
 
2.9%
Other values (96) 3152
62.7%
ValueCountFrequency (%)
0 30
 
0.6%
0.004132231405 39
 
0.8%
0.00826446281 61
 
1.2%
0.01239669421 87
 
1.7%
0.01652892562 179
3.6%
0.02066115702 144
2.9%
0.02479338843 128
2.5%
0.02892561983 224
4.5%
0.03305785124 253
5.0%
0.03719008264 188
3.7%
ValueCountFrequency (%)
1 9
0.2%
0.7975206612 8
0.2%
0.7892561983 7
0.1%
0.7314049587 5
0.1%
0.6694214876 12
0.2%
0.6652892562 5
0.1%
0.6363636364 9
0.2%
0.6239669421 9
0.2%
0.5661157025 7
0.1%
0.5578512397 8
0.2%

tourism
Real number (ℝ)

Distinct58
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043023609
Minimum0
Maximum1
Zeros1063
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-06-13T16:53:34.413448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0050761421
median0.010152284
Q30.035532995
95-th percentile0.18274112
Maximum1
Range1
Interquartile range (IQR)0.030456853

Descriptive statistics

Standard deviation0.10254211
Coefficient of variation (CV)2.3833916
Kurtosis36.150866
Mean0.043023609
Median Absolute Deviation (MAD)0.010152284
Skewness5.4302283
Sum216.45178
Variance0.010514884
MonotonicityNot monotonic
2023-06-13T16:53:34.476441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1063
21.1%
0.005076142132 964
19.2%
0.01015228426 490
9.7%
0.0152284264 383
 
7.6%
0.02030456853 308
 
6.1%
0.03045685279 238
 
4.7%
0.02538071066 194
 
3.9%
0.04060913706 153
 
3.0%
0.03553299492 150
 
3.0%
0.05076142132 102
 
2.0%
Other values (48) 986
19.6%
ValueCountFrequency (%)
0 1063
21.1%
0.005076142132 964
19.2%
0.01015228426 490
9.7%
0.0152284264 383
 
7.6%
0.02030456853 308
 
6.1%
0.02538071066 194
 
3.9%
0.03045685279 238
 
4.7%
0.03553299492 150
 
3.0%
0.04060913706 153
 
3.0%
0.04568527919 99
 
2.0%
ValueCountFrequency (%)
1 7
0.1%
0.9644670051 11
0.2%
0.7614213198 8
0.2%
0.7005076142 7
0.1%
0.6751269036 9
0.2%
0.578680203 8
0.2%
0.5329949239 8
0.2%
0.5025380711 9
0.2%
0.4873096447 9
0.2%
0.4670050761 8
0.2%

2013
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
5020 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5020
99.8%
1 11
 
0.2%

Length

2023-06-13T16:53:34.530583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.574207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5020
99.8%
1 11
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5020
99.8%
1 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5020
99.8%
1 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5020
99.8%
1 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5020
99.8%
1 11
 
0.2%

2014
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4963 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4963
98.6%
1 68
 
1.4%

Length

2023-06-13T16:53:34.611576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.655195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4963
98.6%
1 68
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 4963
98.6%
1 68
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4963
98.6%
1 68
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4963
98.6%
1 68
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4963
98.6%
1 68
 
1.4%

2015
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4824 
1
 
207

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4824
95.9%
1 207
 
4.1%

Length

2023-06-13T16:53:34.692769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.736977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4824
95.9%
1 207
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 4824
95.9%
1 207
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4824
95.9%
1 207
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4824
95.9%
1 207
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4824
95.9%
1 207
 
4.1%

2016
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4621 
1
 
410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4621
91.9%
1 410
 
8.1%

Length

2023-06-13T16:53:34.774368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.818801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4621
91.9%
1 410
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 4621
91.9%
1 410
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4621
91.9%
1 410
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4621
91.9%
1 410
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4621
91.9%
1 410
 
8.1%

2017
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4339 
1
692 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4339
86.2%
1 692
 
13.8%

Length

2023-06-13T16:53:34.857737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.902311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4339
86.2%
1 692
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 4339
86.2%
1 692
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4339
86.2%
1 692
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4339
86.2%
1 692
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4339
86.2%
1 692
 
13.8%

2018
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4124 
1
907 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4124
82.0%
1 907
 
18.0%

Length

2023-06-13T16:53:34.941020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:34.985116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4124
82.0%
1 907
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 4124
82.0%
1 907
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4124
82.0%
1 907
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4124
82.0%
1 907
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4124
82.0%
1 907
 
18.0%

2019
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4088 
1
943 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4088
81.3%
1 943
 
18.7%

Length

2023-06-13T16:53:35.024164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:35.068280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4088
81.3%
1 943
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 4088
81.3%
1 943
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4088
81.3%
1 943
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4088
81.3%
1 943
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4088
81.3%
1 943
 
18.7%

2020
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4286 
1
745 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4286
85.2%
1 745
 
14.8%

Length

2023-06-13T16:53:35.106948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:35.151233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4286
85.2%
1 745
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 4286
85.2%
1 745
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4286
85.2%
1 745
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4286
85.2%
1 745
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4286
85.2%
1 745
 
14.8%

2021
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4530 
1
501 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4530
90.0%
1 501
 
10.0%

Length

2023-06-13T16:53:35.189555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:35.233919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4530
90.0%
1 501
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 4530
90.0%
1 501
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4530
90.0%
1 501
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4530
90.0%
1 501
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4530
90.0%
1 501
 
10.0%

2022
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4539 
1
492 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4539
90.2%
1 492
 
9.8%

Length

2023-06-13T16:53:35.272389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:35.316577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4539
90.2%
1 492
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 4539
90.2%
1 492
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4539
90.2%
1 492
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4539
90.2%
1 492
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4539
90.2%
1 492
 
9.8%

2023
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
4976 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4976
98.9%
1 55
 
1.1%

Length

2023-06-13T16:53:35.355103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:53:35.398743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4976
98.9%
1 55
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 4976
98.9%
1 55
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5031
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4976
98.9%
1 55
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4976
98.9%
1 55
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4976
98.9%
1 55
 
1.1%

Interactions

2023-06-13T16:53:27.813104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:39.235497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:40.905641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:42.506056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:44.245817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:45.876064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:47.649146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:49.282622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:51.107069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:52.782940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:54.426796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:56.254790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:57.854497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:59.469520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:01.077718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:02.946837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:04.552827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:06.166076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:07.772506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:09.705710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:11.323968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:12.935650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:14.555158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:16.159604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:18.173913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:19.767654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:21.380772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:22.981266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:24.593459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:26.198396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:27.865604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:39.287007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:40.957739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:42.558216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:44.298188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:45.931818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:47.701310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:49.337909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:51.161643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:52.835818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:54.478411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:56.306161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:57.906654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:59.521186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:01.129501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:03.003056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:04.604714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-06-13T16:52:44.137292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:45.768497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:47.541515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:49.175113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:50.992808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:52.672299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:54.318623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:56.146495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:57.748797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:59.360295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:00.970082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:02.836642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:04.447022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:06.060494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:07.666442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:09.599249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:11.216909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:12.829236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:14.449359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:16.053546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:18.066248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:19.660757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:21.273424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:22.875896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:24.487174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:26.092435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:27.700850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:29.835250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:40.852544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:42.452964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:44.191027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:45.821775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:47.595055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:49.228680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:51.050010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:52.727157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:54.372837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:56.199735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:57.801344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:52:59.413301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:01.023528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:02.889418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:04.499664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:06.113575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:07.719651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:09.652582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:11.270575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:12.882275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:14.502291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:16.106649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:18.120501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:19.714321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:21.327754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:22.928483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:24.539974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:26.145469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-13T16:53:27.760321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-13T16:53:35.474394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
yearmonthstart_indxinflation_rateAverage_incomeapartments_lt_5lvlapartments_me_5lvl_lt_10lvlapartments_me_10lvl_lt_15lvlapartments_me_15lvldormitoryhotelresidentialcommercialofficeretailciviccollegeschooltransportationuniversitystadiumsports_hallpavilionbarfast_foodcaferestaurantcinemaleisuretourismme_mln_flag500k-mln_flag250k-500k_flag100k-250k_flaglt_100k_flagquarterfood_courtexhibition_centre20132014201520162017201820192020202120222023
year1.000-0.1190.1251.0000.067-0.0340.0240.0140.0160.0040.070-0.070-0.0490.066-0.060-0.0010.0030.0360.024-0.0140.0590.0040.081-0.007-0.001-0.075-0.021-0.009-0.007-0.0340.2380.0880.1300.2810.1040.1010.0830.1040.3680.9260.9990.9990.9990.9990.9990.9990.9990.9990.999
month-0.1191.0000.083-0.1190.0080.0020.003-0.003-0.001-0.005-0.006-0.001-0.0020.0050.0100.007-0.0000.004-0.010-0.001-0.004-0.002-0.003-0.008-0.009-0.010-0.005-0.013-0.005-0.0110.0000.0000.0000.0000.0000.9990.0000.0000.0000.0000.0390.0450.0000.0000.0000.0370.0000.0550.156
start_indx0.1250.0831.0000.125-0.007-0.002-0.002-0.007-0.012-0.009-0.0030.002-0.0000.0050.002-0.000-0.001-0.0070.006-0.002-0.000-0.001-0.0050.0070.0020.0030.0060.008-0.010-0.0050.0000.0000.0000.0000.0000.0800.0000.0000.0490.0110.0510.0230.0000.0000.0000.0400.0000.0560.166
inflation_rate1.000-0.1190.1251.0000.067-0.0340.0240.0140.0160.0040.070-0.070-0.0490.066-0.060-0.0010.0030.0360.024-0.0140.0590.0040.081-0.007-0.001-0.075-0.021-0.009-0.007-0.0340.2390.0770.1270.2770.0940.0990.0730.0760.3690.9260.9990.9990.5840.6860.6750.5860.9990.9990.999
Average_income0.0670.008-0.0070.0671.000-0.1690.0430.2270.3450.0270.035-0.330-0.1000.1940.0050.132-0.1140.2860.268-0.025-0.1000.3140.061-0.1460.023-0.327-0.106-0.104-0.070-0.1040.7130.2910.3180.2920.3590.0000.1130.1330.0470.0990.1200.1610.0620.1210.1550.1270.0630.0440.000
apartments_lt_5lvl-0.0340.002-0.002-0.034-0.1691.0000.5670.2390.1030.3670.3920.3780.4670.4150.3830.3090.3400.2920.1050.4120.166-0.1380.1340.1280.3010.3640.3070.1490.4030.3560.1570.1390.1710.1150.2130.0000.2210.0810.0000.0850.0660.0870.0930.0470.0820.0460.0760.0930.083
apartments_me_5lvl_lt_10lvl0.0240.003-0.0020.0240.0430.5671.0000.6870.4900.5960.4390.1520.6020.6090.5280.3580.5210.7070.3040.5930.1980.0560.1720.1850.5050.3580.3760.2030.5540.3400.3650.1740.1560.2230.4760.0000.1500.1230.0750.0820.0890.0760.1080.0780.1070.1290.0830.0740.028
apartments_me_10lvl_lt_15lvl0.014-0.003-0.0070.0140.2270.2390.6871.0000.8290.4820.294-0.0770.4710.5310.5150.3120.3690.7130.3490.4750.1180.2720.0590.0880.4470.1650.2430.1090.4150.1960.5400.2260.2000.2210.4800.0000.1560.1610.0670.0380.0850.0850.0580.0220.1230.1630.0990.1130.000
apartments_me_15lvl0.016-0.001-0.0120.0160.3450.1030.4900.8291.0000.3900.269-0.1400.3500.4480.4760.2640.2540.6580.3280.3650.0800.3490.033-0.0210.3900.0730.1920.0630.3350.1020.5360.1580.1900.2750.3690.0000.1480.3690.0120.0550.0610.0940.0590.1040.1120.1470.0810.0730.067
dormitory0.004-0.005-0.0090.0040.0270.3670.5960.4820.3901.0000.4070.2050.4660.5370.4280.2940.4090.5550.1980.5780.1630.0660.2940.1070.4950.3180.3260.1790.5100.3540.3060.1940.1350.1570.3130.0000.1460.2670.0000.0420.0500.0870.0940.0660.0360.0860.1170.0950.060
hotel0.070-0.006-0.0030.0700.0350.3920.4390.2940.2690.4071.0000.1930.5080.5450.4660.4130.3590.4290.3110.5060.2140.0390.1040.0710.3650.3800.3970.2210.3800.4180.1510.1670.0250.0310.2000.0000.1350.0580.0000.0290.0570.1370.0360.0450.0660.0830.0540.0380.000
residential-0.070-0.0010.002-0.070-0.3300.3780.152-0.077-0.1400.2050.1931.0000.3030.1030.1600.0270.1550.002-0.0580.2390.124-0.2160.1330.1500.1310.2760.2130.1380.2930.2270.0950.1060.0540.0780.1020.0000.0340.0000.0000.0000.0100.0360.0250.1130.0470.1290.0000.0490.000
commercial-0.049-0.002-0.000-0.049-0.1000.4670.6020.4710.3500.4660.5080.3031.0000.5450.5710.3800.4910.5340.3220.6400.1930.100-0.0510.2220.4390.4760.4380.2400.4830.4410.1890.0700.2010.1070.1740.0000.3980.0000.0000.0210.0810.1050.0540.0730.0800.1080.1060.0540.000
office0.0660.0050.0050.0660.1940.4150.6090.5310.4480.5370.5450.1030.5451.0000.5130.4140.3620.5670.3630.5620.1160.1400.1630.1320.4560.3060.3540.2280.4610.3620.3430.1520.0930.1680.2380.0000.2080.2310.0000.0000.0520.0570.0570.0790.0500.1850.1060.0910.035
retail-0.0600.0100.002-0.0600.0050.3830.5280.5150.4760.4280.4660.1600.5710.5131.0000.3860.4010.5240.2530.4480.1690.176-0.0220.1610.4290.3890.3410.1700.3310.2820.1400.2030.0870.0830.2010.0000.1600.1340.0000.0530.0230.0490.0680.1030.0800.0930.0000.0360.000
civic-0.0010.007-0.000-0.0010.1320.3090.3580.3120.2640.2940.4130.0270.3800.4140.3861.0000.2620.3910.2630.3820.1810.155-0.013-0.0110.3140.1780.1900.0880.2560.3120.1320.2210.0960.0690.1080.0000.0710.1730.0000.0260.0730.1200.0480.1090.0510.0620.0570.0620.000
college0.003-0.000-0.0010.003-0.1140.3400.5210.3690.2540.4090.3590.1550.4910.3620.4010.2621.0000.4940.1920.5090.2070.093-0.0520.2010.2390.3540.2580.2320.3940.3130.1670.2520.1070.0630.3040.0000.1510.0500.0200.0000.0360.0650.1290.0000.0720.0680.0950.0830.000
school0.0360.004-0.0070.0360.2860.2920.7070.7130.6580.5550.4290.0020.5340.5670.5240.3910.4941.0000.3730.5960.1200.2970.1080.0880.4500.2550.3770.2030.5290.3270.5630.1520.1680.2230.5710.0000.1540.1450.0000.0570.0660.1320.0980.1240.0700.1560.1490.0990.000
transportation0.024-0.0100.0060.0240.2680.1050.3040.3490.3280.1980.311-0.0580.3220.3630.2530.2630.1920.3731.0000.2620.0690.1270.0740.0210.2910.1160.1450.0430.2030.2380.4380.1480.1520.1510.1790.0000.1060.2300.0000.0000.0390.0320.1360.0690.1120.1300.0510.0480.000
university-0.014-0.001-0.002-0.014-0.0250.4120.5930.4750.3650.5780.5060.2390.6400.5620.4480.3820.5090.5960.2621.0000.2200.1350.0400.1620.4430.4360.4430.2860.5370.4740.2510.3050.1200.1620.3010.0000.1930.1050.0000.0230.0550.1300.1080.0000.0810.1140.1100.1020.022
stadium0.059-0.004-0.0000.059-0.1000.1660.1980.1180.0800.1630.2140.1240.1930.1160.1690.1810.2070.1200.0690.2201.0000.0080.0530.0070.1180.1320.1130.0430.1750.1260.1190.2420.0710.0250.1330.0000.0610.1610.0170.0790.0590.0690.1110.0490.0280.0940.0500.0770.152
sports_hall0.004-0.002-0.0010.0040.314-0.1380.0560.2720.3490.0660.039-0.2160.1000.1400.1760.1550.0930.2970.1270.1350.0081.000-0.112-0.0540.070-0.1190.0670.009-0.025-0.0240.2750.0840.0770.0720.1380.0000.3530.2750.0000.0210.0860.0210.0740.1400.0090.0720.1200.0490.000
pavilion0.081-0.003-0.0050.0810.0610.1340.1720.0590.0330.2940.1040.133-0.0510.163-0.022-0.013-0.0520.1080.0740.0400.053-0.1121.000-0.0510.2820.0480.046-0.0590.2610.0760.1460.0400.1490.0430.0720.0000.0000.0990.0000.0000.0000.0210.0570.0510.0790.0660.0150.1000.000
bar-0.007-0.0080.007-0.007-0.1460.1280.1850.088-0.0210.1070.0710.1500.2220.1320.161-0.0110.2010.0880.0210.1620.007-0.054-0.0511.0000.1780.3730.2430.1500.2030.1000.0660.1870.1010.0860.1200.0000.1440.0970.0000.1240.0970.0450.0870.0200.0780.0900.0060.0700.000
fast_food-0.001-0.0090.002-0.0010.0230.3010.5050.4470.3900.4950.3650.1310.4390.4560.4290.3140.2390.4500.2910.4430.1180.0700.2820.1781.0000.4820.4050.1450.4700.3310.2460.1550.1070.1160.2490.0000.2540.2530.0650.0100.0450.0380.0890.1040.0940.1030.1150.0760.000
cafe-0.075-0.0100.003-0.075-0.3270.3640.3580.1650.0730.3180.3800.2760.4760.3060.3890.1780.3540.2550.1160.4360.132-0.1190.0480.3730.4821.0000.4950.3440.4040.3620.1150.4150.2020.0990.2250.0000.2230.1070.0410.0800.0550.0960.1030.0490.0530.0820.0860.0470.035
restaurant-0.021-0.0050.006-0.021-0.1060.3070.3760.2430.1920.3260.3970.2130.4380.3540.3410.1900.2580.3770.1450.4430.1130.0670.0460.2430.4050.4951.0000.2560.4290.3760.1170.2550.2400.0670.2690.0000.3180.1850.0710.0760.1130.1390.0850.0980.0460.1280.1070.1080.034
cinema-0.009-0.0130.008-0.009-0.1040.1490.2030.1090.0630.1790.2210.1380.2400.2280.1700.0880.2320.2030.0430.2860.0430.009-0.0590.1500.1450.3440.2561.0000.2550.3170.1380.1710.1610.0880.1970.0000.2030.0780.0580.0240.0430.0650.0220.0650.0280.1310.1470.0850.020
leisure-0.007-0.005-0.010-0.007-0.0700.4030.5540.4150.3350.5100.3800.2930.4830.4610.3310.2560.3940.5290.2030.5370.175-0.0250.2610.2030.4700.4040.4290.2551.0000.4580.1980.2830.1560.1040.3110.0000.2080.0890.0470.0910.0830.0470.0560.0650.0840.1170.0530.1220.000
tourism-0.034-0.011-0.005-0.034-0.1040.3560.3400.1960.1020.3540.4180.2270.4410.3620.2820.3120.3130.3270.2380.4740.126-0.0240.0760.1000.3310.3620.3760.3170.4581.0000.1910.1730.0690.1010.1320.0000.3930.0000.1800.0000.0610.0610.0860.0870.0330.1270.1520.0630.066
me_mln_flag0.2380.0000.0000.2390.7130.1570.3650.5400.5360.3060.1510.0950.1890.3430.1400.1320.1670.5630.4380.2510.1190.2750.1460.0660.2460.1150.1170.1380.1980.1911.0000.2840.2560.2950.4260.0000.0850.1080.0000.0510.1060.1280.0520.0420.1280.1120.0190.0000.000
500k-mln_flag0.0880.0000.0000.0770.2910.1390.1740.2260.1580.1940.1670.1060.0700.1520.2030.2210.2520.1520.1480.3050.2420.0840.0400.1870.1550.4150.2550.1710.2830.1730.2841.0000.1420.1650.2380.0000.1280.0620.0000.0210.0000.0640.0150.0000.0410.0180.0260.0230.017
250k-500k_flag0.1300.0000.0000.1270.3180.1710.1560.2000.1900.1350.0250.0540.2010.0930.0870.0960.1070.1680.1520.1200.0710.0770.1490.1010.1070.2020.2400.1610.1560.0690.2560.1421.0000.1480.2140.0000.0210.0510.0410.0000.0000.0700.0410.0460.0330.0780.0000.0500.000
100k-250k_flag0.2810.0000.0000.2770.2920.1150.2230.2210.2750.1570.0310.0780.1070.1680.0830.0690.0630.2230.1510.1620.0250.0720.0430.0860.1160.0990.0670.0880.1040.1010.2950.1650.1481.0000.2470.0000.0850.0840.0000.0590.1690.1430.0780.0000.0930.0700.0640.0870.033
lt_100k_flag0.1040.0000.0000.0940.3590.2130.4760.4800.3690.3130.2000.1020.1740.2380.2010.1080.3040.5710.1790.3010.1330.1380.0720.1200.2490.2250.2690.1970.3110.1320.4260.2380.2140.2471.0000.0000.1100.0880.0180.0100.0310.0150.0530.0000.0000.0190.0000.0820.017
quarter0.1010.9990.0800.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0230.0000.0500.0500.0180.0000.0000.0470.0000.0550.147
food_court0.0830.0000.0000.0730.1130.2210.1500.1560.1480.1460.1350.0340.3980.2080.1600.0710.1510.1540.1060.1930.0610.3530.0000.1440.2540.2230.3180.2030.2080.3930.0850.1280.0210.0850.1100.0001.0000.1050.0000.0260.0250.0770.0800.0340.0000.0450.0650.0510.000
exhibition_centre0.1040.0000.0000.0760.1330.0810.1230.1610.3690.2670.0580.0000.0000.2310.1340.1730.0500.1450.2300.1050.1610.2750.0990.0970.2530.1070.1850.0780.0890.0000.1080.0620.0510.0840.0880.0000.1051.0000.0000.0000.0240.0460.1170.0220.0240.0490.0580.0410.000
20130.3680.0000.0490.3690.0470.0000.0750.0670.0120.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0170.0000.0000.0000.0650.0410.0710.0580.0470.1800.0000.0000.0410.0000.0180.0230.0000.0001.0000.0000.0000.0000.0000.0080.0100.0000.0000.0000.000
20140.9260.0000.0110.9260.0990.0850.0820.0380.0550.0420.0290.0000.0210.0000.0530.0260.0000.0570.0000.0230.0790.0210.0000.1240.0100.0800.0760.0240.0910.0000.0510.0210.0000.0590.0100.0000.0260.0000.0001.0000.0140.0280.0420.0510.0520.0440.0330.0330.000
20150.9990.0390.0510.9990.1200.0660.0890.0850.0610.0500.0570.0100.0810.0520.0230.0730.0360.0660.0390.0550.0590.0860.0000.0970.0450.0550.1130.0430.0830.0610.1060.0000.0000.1690.0310.0500.0250.0240.0000.0141.0000.0580.0800.0950.0970.0840.0660.0650.009
20160.9990.0450.0230.9990.1610.0870.0760.0850.0940.0870.1370.0360.1050.0570.0490.1200.0650.1320.0320.1300.0690.0210.0210.0450.0380.0960.1390.0650.0470.0610.1280.0640.0700.1430.0150.0500.0770.0460.0000.0280.0581.0000.1170.1380.1410.1220.0970.0960.024
20170.9990.0000.0000.5840.0620.0930.1080.0580.0590.0940.0360.0250.0540.0570.0680.0480.1290.0980.1360.1080.1110.0740.0570.0870.0890.1030.0850.0220.0560.0860.0520.0150.0410.0780.0530.0180.0800.1170.0000.0420.0800.1171.0000.1860.1910.1650.1310.1300.037
20180.9990.0000.0000.6860.1210.0470.0780.0220.1040.0660.0450.1130.0730.0790.1030.1090.0000.1240.0690.0000.0490.1400.0510.0200.1040.0490.0980.0650.0650.0870.0420.0000.0460.0000.0000.0000.0340.0220.0080.0510.0950.1380.1861.0000.2240.1940.1540.1530.045
20190.9990.0000.0000.6750.1550.0820.1070.1230.1120.0360.0660.0470.0800.0500.0800.0510.0720.0700.1120.0810.0280.0090.0790.0780.0940.0530.0460.0280.0840.0330.1280.0410.0330.0930.0000.0000.0000.0240.0100.0520.0970.1410.1910.2241.0000.1990.1580.1570.046
20200.9990.0370.0400.5860.1270.0460.1290.1630.1470.0860.0830.1290.1080.1850.0930.0620.0680.1560.1300.1140.0940.0720.0660.0900.1030.0820.1280.1310.1170.1270.1120.0180.0780.0700.0190.0470.0450.0490.0000.0440.0840.1220.1650.1940.1991.0000.1370.1360.039
20210.9990.0000.0000.9990.0630.0760.0830.0990.0810.1170.0540.0000.1060.1060.0000.0570.0950.1490.0510.1100.0500.1200.0150.0060.1150.0860.1070.1470.0530.1520.0190.0260.0000.0640.0000.0000.0650.0580.0000.0330.0660.0970.1310.1540.1580.1371.0000.1070.028
20220.9990.0550.0560.9990.0440.0930.0740.1130.0730.0950.0380.0490.0540.0910.0360.0620.0830.0990.0480.1020.0770.0490.1000.0700.0760.0470.1080.0850.1220.0630.0000.0230.0500.0870.0820.0550.0510.0410.0000.0330.0650.0960.1300.1530.1570.1360.1071.0000.028
20230.9990.1560.1660.9990.0000.0830.0280.0000.0670.0600.0000.0000.0000.0350.0000.0000.0000.0000.0000.0220.1520.0000.0000.0000.0000.0350.0340.0200.0000.0660.0000.0170.0000.0330.0170.1470.0000.0000.0000.0000.0090.0240.0370.0450.0460.0390.0280.0281.000

Missing values

2023-06-13T16:53:29.955665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T16:53:30.362718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmonthstart_indxinflation_rateAverage_incomeme_mln_flag500k-mln_flag250k-500k_flag100k-250k_flaglt_100k_flagquarterapartments_lt_5lvlapartments_me_5lvl_lt_10lvlapartments_me_10lvl_lt_15lvlapartments_me_15lvldormitoryhotelresidentialcommercialofficeretailciviccollegeschooltransportationuniversitystadiumsports_hallpavilionbarfast_foodcafefood_courtrestaurantcinemaexhibition_centreleisuretourism20132014201520162017201820192020202120222023
258420180.8181820.8181820.4563310.3464540.00.00.00.01.01.0000000.0196590.0580730.0018550.0000000.0000000.0000000.0001800.0129030.0060850.0070420.0000000.0588240.0000000.0000000.0000000.0000000.0000.0000000.0000000.0000000.0454550.00.00000.0000000.00.0123970.00000000000100000
632920210.3636360.0000000.6446060.1045660.01.00.00.00.00.3333330.1310620.4416080.3283860.0921790.1428570.1724140.0032420.0967740.0344830.1352110.0512820.3529410.2711860.0256410.0222220.4285710.0000.0000000.3333330.2580650.3409090.00.37500.3333330.00.0537190.03045700000000100
676720220.4545450.4545450.7845280.3849090.00.00.00.01.00.3333330.0380080.0427570.0389610.0726260.0000000.0172410.0043230.0354840.0060850.1366200.0000000.0000000.0338980.0000000.0000000.0000000.0000.0000000.2222220.0967740.1590910.00.12500.0000000.00.0206610.00507600000000010
585820210.0000000.5454550.6446060.9575951.00.00.00.00.00.0000000.0563560.5360560.3506490.3519550.1607140.0000000.0001800.0193550.0405680.1183100.0000000.2941180.5762710.0000000.0222220.0000000.1250.0000000.0000000.0967740.0227270.00.00000.0000000.00.0743800.00000000000000100
134920170.2727270.7272730.4199650.1705240.00.00.00.01.00.3333330.0039320.0178690.0000000.0000000.0000000.0000000.0023410.0043010.0000000.0042250.0000000.0000000.0508470.0000000.0000000.0000000.0000.0000000.0000000.0000000.0909090.00.06250.0000000.00.1115700.02538100001000000
594020210.0000000.7272730.6446060.5285071.00.00.00.00.00.0000000.4062910.2131460.0166980.0000000.0357140.1896550.0205330.0655910.2312370.0577460.0256410.0588240.3474580.1025640.1111110.0000000.0000.0324480.2222220.8387100.4545450.00.87500.1666670.00.5206610.10152300000000100
489020200.1818180.7272730.5665530.0553560.00.00.00.01.00.0000000.0799480.0631780.0204080.0586590.0178570.1896550.0012610.0118280.0973630.0718310.0000000.0882350.0847460.0000000.0000000.0000000.0000.0000000.0000000.0645160.0681820.00.31250.0000000.00.1074380.46700500000001000
287820180.2727270.1818180.4563310.0615620.00.00.00.01.00.3333330.0170380.0344610.0037110.0000000.0000000.0000000.0019810.0021510.0141990.0253520.0000000.0000000.0423730.0000000.0000000.0000000.0000.0000000.0000000.0000000.0454550.00.00000.0000000.00.0495870.00000000000100000
580520211.0000000.9090910.6446060.9575951.00.00.00.00.01.0000000.0065530.1767710.1799630.7988830.5000000.0344830.0000000.0473120.0912780.1267610.0256410.0588240.3474580.0000000.5000000.0000001.0000.0000000.0000000.1612900.0227270.00.06250.1666670.00.0495870.01015200000000100
544620200.8181820.7272730.5665530.2521950.00.00.00.01.01.0000000.1598950.1831530.0278290.0000000.0000000.0172410.0012610.0043010.0060850.0197180.0000000.0000000.1101690.0000000.0000000.0000000.0000.0088500.0000000.0322580.1136360.00.00000.0000000.00.0371900.00507600000001000
yearmonthstart_indxinflation_rateAverage_incomeme_mln_flag500k-mln_flag250k-500k_flag100k-250k_flaglt_100k_flagquarterapartments_lt_5lvlapartments_me_5lvl_lt_10lvlapartments_me_10lvl_lt_15lvlapartments_me_15lvldormitoryhotelresidentialcommercialofficeretailciviccollegeschooltransportationuniversitystadiumsports_hallpavilionbarfast_foodcafefood_courtrestaurantcinemaexhibition_centreleisuretourism20132014201520162017201820192020202120222023
393120190.8181820.8181820.5195030.0303340.01.00.00.00.01.0000000.1022280.1346520.0760670.1452510.3214290.0862070.0025220.0333330.0811360.0985920.0000000.1176470.0932200.0000000.1222220.0000000.0000.00.2222220.0000000.3181820.0000000.00000.1666670.00.1239670.03045700000010000
530720200.0000000.0000000.5665530.1748371.00.00.00.00.00.0000000.1100920.2342050.4192950.3770950.2321430.0517240.0041430.0612900.0730220.1112680.1282050.0588240.4576270.0512820.1555560.0000000.1250.00.1111110.2903230.3863640.0000000.31250.6666670.00.1446280.03045700000001000
65720160.8181820.5454550.3462910.2146320.00.01.00.00.01.0000000.4678900.2386730.0408160.0363130.6250000.0344830.0023410.0215050.0365110.0591550.1538460.1764710.1949150.0512820.0555560.0000000.0000.00.0000000.1290320.1818180.3333330.37500.0000000.00.0909090.06091400010000000
30720150.7272730.5454550.1897140.2818240.01.00.00.00.00.6666670.0720840.1537970.1317250.2122910.1428570.0517240.0014410.0741940.0243410.0535210.1794870.0882350.2372880.0000000.1111110.2857140.0000.00.0000000.0645160.0909090.0000000.06250.0000000.00.2066120.02030500100000000
206420180.4545450.9090910.4563310.0940240.00.00.01.00.00.3333330.1022280.0421190.0389610.0307260.0000000.0000000.0001800.0107530.0040570.0521130.0000000.0588240.0762710.0000000.0000000.0000000.0000.00.0000000.0000000.0909090.0000000.06250.1666670.00.0289260.00507600000100000
620220210.6363640.7272730.6446060.1665630.00.01.00.00.00.6666670.0838790.4588390.1372910.0139660.0000000.0000000.0763690.0075270.0000000.0183100.0256410.0882350.0254240.0000000.0666670.0000000.0000.00.2222220.2580650.2500000.0000000.31250.6666670.00.1363640.01522800000000100
232520180.9090911.0000000.4563310.9575951.00.00.00.00.01.0000000.0419400.1167840.3265310.5893850.0535710.0000000.0001800.0182800.0263690.0887320.0000000.0000000.2711860.0512820.0000000.0000000.0000.00.0000000.1612900.0909090.0000000.18750.1666670.00.0661160.02538100000100000
230320180.0000000.0000000.4563310.7403660.00.00.00.01.00.0000000.0498030.0587110.0000000.0000000.0357140.0000000.0001800.0032260.0040570.0042250.0000000.0000000.0254240.0000000.0000000.0000000.0000.00.2222220.0322580.1363640.0000000.06250.0000000.00.0702480.00507600000100000
339220190.1818180.7272730.5195030.1682910.00.01.00.00.00.0000000.0078640.0095720.0092760.0139660.0535710.0172410.0025220.0129030.0000000.0197180.0000000.1764710.1694920.0000000.1111110.0000000.0000.00.0000000.0000000.1136360.0000000.06250.0000000.00.1239670.04060900000010000
595620210.5454550.8181820.6446061.0000000.00.00.00.01.00.6666670.0065530.0000000.0000000.0000000.0000000.0172410.0001800.0075270.0060850.0323940.0000000.0294120.0338980.0000000.0000000.0000000.0000.00.0000000.0000000.0681820.0000000.00000.0000000.00.0082640.00507600000000100